Device and method for classifying the activity and/or counting steps of a user

ABSTRACT

Device and method for classifying the activity and/or counting steps of a user. A method for classifying the activity of a user can comprise measuring accelerometer data for a plurality of axes; identifying a most active one of the plurality of axes based on the accelerometer data; and classifying the activity of the user based on a signal amplitude of the accelerometer data for the most active axis and one or more threshold values.

CLAIM FOR PRIORITY

This application claims priority to PCT/SG2014/000248, filed 30 May2014.

FIELD OF INVENTION

The present invention relates broadly to device and method forclassifying the activity and/or counting steps of a user.

BACKGROUND

In recent years, people are becoming more health conscious and havetaken up running and walking as a convenient and effective way toexercise. There is also an increased interest in self-tracking of dailyactivities through wearable sensors. Hence there is need for a stepcounting device that can accurately record a wearer's gait status aswell as the number of steps taken.

Conventional step counting devices that use accelerometer(s) typicallyrequire the user to first position the device in a limited set oforientations. For example, the user has to secure the device on theirbody such that a dedicated axis (i.e. a direction of the device) issubstantially and continuously aligned with the direction of thegravitational force throughout the measurement period.

Existing devices are typically unable to detect changes or differentiatesignals that correspond to different gaits of the user, resulting in anincorrect count of the steps taken. Motion noises experienced by thedevices will also cause false steps to be measured and actual steps tobe missed in existing step counting devices.

Also, as the forward and backward swing of a body part to which thedevice is attached is not same for every step, existing devices aretypically unable to correctly identify peaks as a step count due to theinconsistencies in the signal peaks and troughs.

Embodiments of the present invention provide a system and method foractivity monitoring that seek to address at least one of the aboveproblems.

SUMMARY

In accordance with a first aspect of the present invention there isprovided a device for classifying the activity of a user wearing thedevice, the device comprising an accelerometer for measuringaccelerometer data for a plurality of axes; and a processor configuredto:

-   -   identify a most active one of the plurality of axes based on the        accelerometer data; and    -   classify the activity of the user based on a signal amplitude of        the accelerometer data for the most active axis and one or more        threshold values.

In accordance with a second aspect of the present invention there isprovided a device for counting the number of steps taken by a userwearing the device, the device comprising an accelerometer for measuringaccelerometer data for at least one axis; and a processor configured to:

-   -   apply a derivative operator to the accelerometer data in        successive processing windows;    -   count peaks in the derivative of the accelerometer data in each        processing window; and    -   eliminate over counted peaks based on time difference between a        first peak in a current window and a last peak in a preceding        processing window.

In accordance with a third aspect of the present invention there isprovided a method for classifying the activity of a user, the methodcomprising measuring accelerometer data for a plurality of axes;identifying a most active one of the plurality of axes based on theaccelerometer data; and classifying the activity of the user based on asignal amplitude of the accelerometer data for the most active axis andone or more threshold values.

In accordance with a fourth aspect of the present invention there isprovided a method for counting the number of steps taken by a user, themethod comprising measuring accelerometer data for at least one axis;applying a derivative operator to the accelerometer data in successiveprocessing windows; counting peaks in the derivative of theaccelerometer data in each processing window; and eliminating overcounted peaks based on time difference between a first peak in a currentwindow and a last peak in a preceding processing window.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention will be better understood and readilyapparent to one of ordinary skill in the art from the following writtendescription, by way of example only, and in conjunction with thedrawings, in which:

FIG. 1—Shows the process of activity classification according to anexample embodiment.

FIG. 2—Shows a spike in the raw accelerometer data.

FIG. 3—Shows the accelerometer data of FIG. 2 with the attenuated spikeafter applying a smoothing process according to an example embodiment.

FIG. 4—Shows example signal amplitudes and determined characteristics ofan accelerometer signal according to an example embodiment.

FIG. 5—Shows the process of classifying activity using threshold levelsaccording to an example embodiment.

FIG. 6—Shows the signal amplitude measured during different activitiesaccording to an example embodiment.

FIG. 7—Shows the process of peak detection and step count according toan example embodiment.

FIG. 8—Shows the signal waveform of a wrist-worn device before and afterapplying the Pan Tompkins derivation operator according to an exampleembodiment.

FIG. 9—Shows the adaptive threshold value (Δ_(w)) used for processing ofsubsequent window(s) according to an example embodiment.

FIG. 10—Shows peak to peak distance between the peaks for twoconsecutive windows according to an example embodiment.

FIG. 11—Shows the process flow of activity classification and step countare being performed according to an example embodiment.

FIG. 12—Shows schematic drawing of an assembly comprising a wearabledevice and a communication device, according to an example embodiment.

FIG. 13—Shows a block diagram of an assembly comprising a wearabledevice and a communication device, according to an example embodiment.

FIG. 14—Shows a block diagram of a device for classifying the activityof a user according to an example embodiment.

FIG. 15—Shows a block diagram of a device for counting steps of a useraccording to an example embodiment.

FIG. 16—Shows a flow chart illustrating a method for classifying theactivity of a user according to an example embodiment.

FIG. 17—Shows a flow chart illustrating a method for counting steps of auser according to an example embodiment.

DETAILED DESCRIPTION

Embodiments of the present invention relate to a wearable device andmethod for classifying the activity of the user and a wearable deviceand method of counting the number of steps taken by the user. In onenon-limiting example embodiment, the device is worn on the wrist of theuser and is configured to obtain the user's acceleration information forprocessing to classify the activity of the user and/or to count thenumber of steps taken by the user.

The present specification also discloses an apparatus, which may beinternal and/or external to the wearable device in example embodiments,for performing the operations of the methods. Such apparatus may bespecially constructed for the required purposes, or may comprise ageneral purpose computer or other device selectively activated orreconfigured by a computer program stored in the computer. Thealgorithms and displays presented herein are not inherently related toany particular computer or other apparatus. Various general purposemachines may be used with programs in accordance with the teachingsherein. Alternatively, the construction of more specialized apparatus toperform the required method steps may be appropriate. The structure of aconventional general purpose computer will appear from the descriptionbelow. In addition, the present specification also implicitly disclosesa computer program, in that it would be apparent to the person skilledin the art that the individual steps of the method described herein maybe put into effect by computer code. The computer program is notintended to be limited to any particular programming language andimplementation thereof. It will be appreciated that a variety ofprogramming languages and coding thereof may be used to implement theteachings of the disclosure contained herein. Moreover, the computerprogram is not intended to be limited to any particular control flow.There are many other variants of the computer program, which can usedifferent control flows without departing from the spirit or scope ofthe invention.

Furthermore, one or more of the steps of the computer program may beperformed in parallel rather than sequentially. Such a computer programmay be stored on any computer readable medium. The computer readablemedium may include storage devices such as magnetic or optical disks,memory chips, or other storage devices suitable for interfacing with ageneral purpose computer. The computer readable medium may also includea hard-wired medium such as exemplified in the Internet system, orwireless medium such as exemplified in the GSM mobile telephone system.The computer program when loaded and executed on such a general-purposecomputer effectively results in an apparatus that implements the stepsof the preferred method.

The invention may also be implemented as hardware modules. Moreparticular, in the hardware sense, a module is a functional hardwareunit designed for use with other components or modules. For example, amodule may be implemented using discrete electronic components, or itcan form a portion of an entire electronic circuit such as anApplication Specific Integrated Circuit (ASIC). Numerous otherpossibilities exist. Those skilled in the art will appreciate that thesystem can also be implemented as a combination of hardware and softwaremodules.

FIG. 1 shows a flowchart 100 illustrating a method for classifying theactivity of a user according to an example embodiment, the methodcomprising:

At step 102, obtaining the acceleration signals of a user through thewearable device,

at step 104, splitting the acceleration signals data into a plurality ofprocessing windows;

and in each processing window,

at step 106, smoothing the data with a moving average function,

at step 108, the accelerometer data is converted into g value data,

at step 110, identifying the most active axis of the accelerationsignals,

at step 112, classifying the activity of the user based on theacceleration signals from the most active axis,

Details of example features that may be used separately and/or incombination with one or more of the other example features will bedescribed next.

Processing Windows

The size of a window is herein described as the time duration of thewindow, by way of example only. It was found that accuracy suitable forclassifying activities and/or counting of steps can for example, butwithout limitation, be achieved by utilizing window sizes betweenabout >0 to 5 seconds depending on specific accuracy requirement.Generally, a longer window size can lower the chance of an over-countedpeak in a window transition area and may thus increase the accuracy. Ashorter window may better capture the transition between differentactivities, which can happen within a very short period of time.

Smoothing of Accelerometer Data

Raw accelerometer data are typically noisy. A stray spike 200 in exampleaccelerometer data 202 is shown in FIG. 2. The stray spike 200 mayresult in the wrong axis being identified as the most active axis forthe subsequent processing. In example embodiments, smoothing isadvantageously applied to the raw accelerometer data 202. Preferably,smoothing is achieved by applying a moving average operation to the rawaccelerometer data 200.

FIG. 3 shows the accelerometer data 202* after smoothing according toone example embodiment, with an attenuated spike 200* that preferablydoes not affect the identification of the most active axis for thesubsequent processing.

Most Active Axis

The most active axis in example embodiments is identified as theaccelerometer axis with the greatest amplitude signal, representative ofthe contributory effect of the gravitational force and the impact forceof the wearer's foot on the ground. In other word, the most active axisin example embodiments the accelerometer axis that is closest to thevertical direction, i.e. substantially perpendicular to the earth.

Advantageously, the most active axis can be easily identified over theother axes regardless of the position or orientation in which the deviceis being used or worn by the user.

The most active axis in a preferred embodiment is selected by comparingthe smoothed x, y and z-axis (i.e. the three accelerometer axesirrespective of actual orientation relative to the ground) data in eachwindow, referred to herein as the moving average. The absolute value ofeach axis from each window is determined, referred to herein as theabsolute moving average. The mean value of each axis based on theabsolute value is then determined, referred to herein as the meanabsolute moving average. From a comparison of the three mean values, theaxis with the largest mean value is identified as the most active axisfor each window, in a preferred embodiment.

Activity Classification

The user's activity is classified into light activity, moderate activityor heavy activity state using the moving average data from the mostactive axis in example embodiments.

In one embodiment, the signal amplitude of the most active axis iscalculated by subtracting the mean 400 or minimum (min) g-value 402 fromthe maximum (max) g-value 404 of most active axis data 406 in, forexample, a window 408 with a size between about >0 to 5 s, as shown inFIG. 4.

FIG. 5 shows a flowchart 500 illustrating the process of classifyingactivity using threshold levels, according to an example embodiment. Inthe example case that the device is wrist-worn by the user, experimentalresults (see FIG. 6) has shown that, for example, the threshold levelsof 0.1 g and 1 g can be used for activity classification.

At step 502, the signal amplitude (SA) of the most active axis isdetermined. In step 504, if SA is smaller than a threshold 1, forexample SA<0.1 g, classification into the light activity state isperformed, see step 506. In step 508, if SA is smaller than a threshold2, for example SA<1 g, classification into the moderate activity stateis performed, see step 510. In other words, in one example for 0.1g≤SA≤1 g, the classification is into the moderate activity state. If SAthe threshold 2, for example SA≥1 g, classification into the heavyactivity state is performed, see step 512.

Examples for the Classifications Include:

Light activity—Stationary motions such as standing still, waiting fortraffic lights to turn green or typing (e.g. on desk),

Moderate activity—Slow walking, brisk walk

Heavy activity—Jogging, running

Most other activities e.g. climbing stairs, cycling, swimming, playingfootball, which involve the movement of the body can also beclassifiable in example embodiment, e.g. based on:

-   -   Further splitting into multiple SA thresholds (on top of the        thresholds 1 & 2 in the example above) that corresponds to each        of the different activities and/or    -   Further processing of the above 3 generic classifications can be        performed in example embodiments by:    -   Filtering and identification using frequency classifications        and/or signal matching techniques.

With reference to FIG. 6, plots 600 and 602 show the SA for the mostactive axis calculated based on the maximum value minus the minimumvalue in the processing window, and calculated based on the maximumvalue minus the mean value in the processing window, respectively. Ascan be seen from FIG. 6, basing the SA on the maximum value minus themean value in the processing window can be preferred for classificationbased on thresholds, as it results in a smoother plot with reducedabsolute values and narrower bands of values in the respective activityregions,

FIG. 7 shows a flow chart 700 illustrating a process for counting stepsaccording to an example embodiment. At step 702, the accelerometer datais captured. At step 704, the data is split into process windows. Atstep 706, low pass filtering is applied to the data from the most activeaxis based on classified activity. At step 708, a derivative of thefiltered data using Pan Tompkins derivative operator is computed. Atstep 710, the output of the derivative signal is normalized. At step712, peak detection is performed for step counting. At step 714, anadaptive threshold value is determined based on the current processwindow and updated to be used for the next process window. At step 716,over counted peaks are eliminated.

Details of example features that may be used separately and/or incombination with one or more of the other example features will bedescribed next.

Low Pass Filter Based on Activity Type (Step 706, FIG. 7)

The cut-off frequency of the low pass filter is preferably adjustedbased on the activity type to remove the correct frequency range ofmotion noise. Choosing a correct cut-off frequency can advantageouslyimprove the signal geometry for avoiding false peaks or undetected peaksduring the subsequent peak detection processing.

In example embodiments, the activity type may be input by the user, ormay be identified using the activity classification method and devicedescribed above, or by any other method or device.

Pan Tompkins Derivative Operator (Step 708, FIG. 7)y(n)=(⅛)[2x(n)+x(n−1)−x(n−3)−2x(n−4)]  Equation (1).where n denotes the data point, and n>4

Pan Tompkins Derivative operator in equation (1) is used in a preferredembodiment to further improve the accuracy of peak detection byadvantageously enlarging the slope information and optimizing the signalcondition for peak detection.

During normal walking and running, for example arm swings are usually insync with the opposite leg. For example, the right arm swings forwardwhen the left feet steps forward and vice versa. Based on this reason,one acceleration peak is detected in e.g. a wrist-worn device for everystep taken by the user.

Acceleration changes can also depend on the change in hand swing andimpact force of feet. During normal walking condition, the impact forceis typically not strong enough and most of the acceleration changes ine.g. a wrist-worn device are due to the hand swing. As the forward andbackward swing of the hand is typically not the same for each step, theinconsistent peaks and troughs of the signal waveform can make itdifficult to implement an accurate peak detection algorithm, and in apreferred embodiment, the Pan Tompkins derivation operator results in amore consistent peaks and troughs pattern. FIGS. 8a ) and b) show thesignal waveform of a wrist-worn device before (curve 800) and after(curve 802) applying Pan Tompkins derivation operator according to anexample embodiment, respectively.

Normalization (Step 710, FIG. 7)

The normalization process in example embodiments is preferred such thatthe derivative signals are scaled correctly for the peak detectionprocessing. In one embodiment, the signal is normalized by firstsubtracting the minimum value of the signal from the signal and thendividing by the maximum value of the subtracted signal.

Peak Detection (Step 712, FIG. 7)

A peak will be detected (i.e. a step counted), when the normalizedderivative signal exceeds a threshold level, according to exampleembodiments. As the data is now normalized (for example min value=0, maxvalue=1), a threshold value Δ₁ for a first processing window can e.g. beset to any pre-determined value between >0 and 0.5.

Determining and Updating Adaptive Threshold (Step 714, FIG. 7)

As shown in FIG. 9, an adaptive threshold value (Δ_(w)) will bedetermined and used for subsequent windows, which can be set at anypre-determined value between about >0% and 50% of the difference betweenthe last peak and valley values of the current window. In one example:Δ_(w)=(a−b)*0.5  Equation (2):where Δ_(w)=adaptive threshold value of a next window w, a=last peakvalue in a current window (w−1), and b=last valley value in the saidcurrent window (w−1), and w≥2.

Elimination of Over Counted Peaks (Step 716, FIG. 7)

It was found by the inventors that the Pan Tompkins derivative operationcan have a tendency to generate a false first peak in the subsequentwindows which can result in over counting of peaks, i.e. steps.

FIG. 10 illustrates the method of advantageously identifying such falsepeaks to be removed from the count in a preferred embodiment. The falsepeaks are identified by checking the distance d between a first peak1000 of the current measured window 1002 and the last peak 1004 of theprevious window 1006.

A threshold may be set at 4 steps per second in one embodiment. Forexample, in the data point domain of a sampling rate of 80 Hz, thedistance threshold may be set at 20 data points based on 4 steps persecond. If the peak to peak distance d is smaller than 20 data points,the current peak 1200 is identified as false and is removed from thestep count.

FIG. 11 shows a flow chart illustrating the process flow 1100 of oneembodiment, in which both the activity classification and the activitybased step counting techniques are integrated into a single wristwearable device.

At step 1102, acceleration data of the user is captured by the devicethrough a 3-axis accelerometer. At step 1104, the accelerometer signalis split into processing windows between about >0 to 5 seconds. At step1106, the signal in each window is smoothed by performing a movingaverage function. At step 1108, the most active axis is identified bycomparing the mean absolute moving average value of each axis. The axiswith the largest value will be the most active axis.

At steps 1110 and 1114, the activity in each window is classified bycalculating the signal amplitude (max-mean) of the moving average signalof the most active axis. Classification will be based on 0.1 g and 1 gthresholds in one non-limiting example. Once a low activity state isidentified (step 1110), the process will move on to the next window(step 1112), as no steps are required to be counted.

For moderate and heavy activity states (step 1114), the step counting isperformed. At step 1116 and 1118, the cut-off frequency of the low passfilter for each window will be updated based on the activity identified,and the low pass filtering applied accordingly.

At step 1120, the Pan Tompkins derivative operator is then applied. Atstep 1122, the derivative signal is then normalized to adjust the signalamplitude within, for example, 0 to 1 in each window (first subtractingthe minimum value of the signal from the signal and then dividing by themaximum value of the subtracted signal in one non-limiting example).

At step 1124, peak detection is then performed where each peak detectedcontributes to a step count. In the first window after transiting to amoderate or heavy activity state from a light activity state, thethreshold will be set between >0 to 0.5 in one non-limiting example. Inother words, a first window transiting from a light activity state doesnot typically require step elimination. For subsequent windows (be itbeing a moderate or heavy activity state), over counted peak detectionand elimination is advantageously performed until a light activity stateis detected for the window. In the subsequent windows, an adaptivethreshold is used and will be set between about >0 to 50% of the heightdifference between the last peak and valley of the previous window, inone non-limiting example, step 1126.

At step 1128, false peaks originating from the Pan Tompkins operationare then removed using e.g. a peak to peak distance (current windowfirst peak vs. previous window last peak) threshold. The threshold inone non-limiting example is based on 4 steps per second. For example, inthe data point domain of a sampling rate of 80 Hz, the distancethreshold is set at 20 data point. If the peak to peak distance issmaller than 20 data points, the current peak is identified as false andis removed from the step count.

The process for the next window will loop back and follow through steps1106 to 1128 described above.

FIG. 12 shows an assembly 1200 comprising a wearable device in the formof a wrist watch 1201 according to an example embodiment. It will beappreciated that in different embodiments the device may also be in anyother form suitable to be worn on any part of the user's body such ashis/her arms, waist, hip or foot. The wrist watch 1201 classifies theuser's activity and/or computes the number of steps taken by the userand communicates the result(s) wirelessly to a telecommunication deviceof the assembly 1200 such as a mobile phone 1202 or other portableelectronic devices, or computing devices such as desk top computers,laptop computer, tab computers etc.

FIG. 13 shows a schematic block diagram of an assembly 1300 comprising awearable device 1301 according to an example embodiment, for classifyingactivity and/or counting steps of the user. The device 1301 includes asignal sensing module 1302, such as an accelerometer or gyroscope, forobtaining the acceleration information of the user.

One non-limiting example of a preferred accelerometer that can beadapted for use in the device is a triple-axis accelerometer MMA8652FCavailable from Freescale Semiconductor, Inc. This accelerometer canprovide the advantage of measuring acceleration in all three directionswith a single package. Alternatively, several single-axis accelerometersoriented to provide three-axis sensing can be used in differentembodiments.

The device 1301 also includes a data processing and computational module1304, such as a processor, which is arranged to receive and process theacceleration information from the signal sensing module 1302. The device1301 also includes a display unit 1306 for displaying a result to a userof the device 1301. The device 1301 in this embodiment further includesa wireless transmission module 1308 arranged to communicate wirelesslywith a telecommunications device 1310 of the assembly 1300. Thetelecommunication device 1310 includes a wireless receiver module 1312for receiving signals from the wearable device 1301 and a display unit1314 for displaying a result to a user of the telecommunication device1310.

FIG. 14 shows a block diagram of a device 1400 for classifying theactivity of a user wearing the device, according to one embodiment. Thedevice 1400 comprises an accelerometer 1402 for measuring accelerometerdata for a plurality of axes; and a processor 1404 configured toidentify a most active one of the plurality of axes based on theaccelerometer data and to classify the activity of the user based on asignal amplitude of the accelerometer data for the most active axis andone or more threshold values.

The processor 1404 may be configured to apply a smoothing to theaccelerometer data prior to identifying the most active axis.

The processor 1404 may be configured to identify the most active axis bydetermining the axis having the largest mean absolute amplitude of thesmoothed signal, which may also be referred to as the largest meanabsolute moving average signal value.

The processor 1404 may be configured to classify the activity based on amaximum signal amplitude of the accelerometer data for the most activeaxis.

The processor 1404 may be configured to classify the activity based onthe maximum signal amplitude of the accelerometer data minus the meansignal amplitude for the most active axis.

The processor 1404 may be configured to identify the most active axisand to classify the activity of the user based on accelerometer datameasured over a predetermined processing window.

The processing window may be in a range from about >0 to 5 seconds.

FIG. 15 shows a block diagram of a device 1500 for counting the numberof steps taken by a user wearing the device, according to oneembodiment. The device 1500 comprises an accelerometer 1502 formeasuring accelerometer data for at least one axis; and a processor 1504configured to apply a derivative operator to the accelerometer data insuccessive processing windows, to count peaks in the derivative of theaccelerometer data in each processing window, and to eliminate overcounted peaks based on time difference between a first peak in a currentwindow and a last peak in a preceding processing window.

The processor 1504 may be configured to count the peaks based on a firstthreshold value if the current processing window is a first processingwindow after transiting from a light activity state to a moderate or aheavy activity state.

The processor may further be configured to update an adaptive thresholdfor the next window. The adaptive threshold for the next window may bebased on the difference in signal amplitude for a last successive peakand valley pair in the current processing window. The adaptive thresholdfor the next window may be between about >0 to 50% of the difference insignal amplitude for the last successive peak and valley pair in thecurrent processing window.

The processor 1504 may be configured to eliminate peaks from the peakcount in the current window if the time difference between the firstpeak in the current window and the last peak in the preceding processingwindow is smaller than a second threshold value. The second thresholdvalue may be about ¼ seconds. The derivative operator may comprise a PanTomkins derivative operator.

The devices 1400, 1500 may be implemented in a wearable device.

The devices 1400, 1500 may be implemented in an assembly comprising awearable device and a communication device.

The devices 1400, 1500 may be implemented in an assembly comprising awearable device and a wireless communication device.

FIG. 16 shows a flow chart 1600 illustrating a method for classifyingthe activity of a user, according to an example embodiment. At step1602, accelerometer data is measured for a plurality of axes. At step1604, a most active one of the plurality of axes is identified based onthe accelerometer data. At step 1606, the activity of the user isclassified based on a signal amplitude of the accelerometer data for themost active axis and one or more threshold values.

The method may comprise applying a smoothing to the accelerometer dataprior to identifying the most active axis.

The method may comprise identifying the most active axis by determiningthe axis having the largest mean absolute value of the smoothed signal,which may also be referred to as the largest mean absolute movingaverage signal value. The method may comprise classifying the activitybased on a maximum signal amplitude of the accelerometer data for themost active axis.

The method may comprise classifying the activity based on the maximumsignal amplitude of the accelerometer data minus the mean signalamplitude for the most active axis.

The method may comprise identifying the most active axis and classifyingthe activity of the user based on accelerometer data measured over apredetermined processing window.

The processing window may be in a range from about >0 to 5 seconds.

FIG. 17 shows a flow chart 1700 illustrating a method for counting thenumber of steps taken by a user. At step 1702, accelerometer data ismeasured for at least one axis. At step 1704, a derivative operator isapplied to the accelerometer data in successive processing windows. Atstep 1706, peaks in the derivative of the accelerometer data in eachprocessing window are counted. At step 1708, over counted peaks areeliminated based on time difference between a first peak in a currentwindow and a last peak in a preceding processing window.

The method may comprise counting the peaks based on a first threshold ifthe current processing window is a first processing window aftertransiting from a light activity state to a moderate or a heavy activitystate.

The method may further comprise updating an adaptive threshold for thenext window. The adaptive threshold for the next window may be based onthe difference in signal amplitude for a last successive peak and valleypair in the current processing window. The adaptive threshold for thenext window may be between about >0 to 50% of the difference in signalamplitude for the last successive peak and valley pair in the currentprocessing window.

The method may comprise eliminating peaks from the peak count in thecurrent processing window if the time difference between the first peakin the current processing window and the last peak in the precedingprocessing window is smaller than a second threshold value. The secondthreshold value may be about ¼ seconds.

The derivative operator may comprise a Pan Tomkins derivative operator.

Example embodiments of the present invention advantageously do notrequire the device to be attached at a fixed orientation throughout themonitoring period. This is because the device in example embodimentsconstantly detects within processing windows for the most active axis(which will be the one closest to the direction of the impact force), tobe used for subsequent processes. The most active axis can be identifiedin example embodiments by looking for the axis having the largest meanabsolute moving average acceleration signal within each window.

Embodiments of the present invention can advantageously be able todetect changes and differentiate the signals corresponding to light,moderate or heavy states by splitting the accelerometer data intoprocess windows and observing the signal amplitude (e.g. max-mean ormax-min) against threshold levels (e.g. 0.1 g and 1 g) within eachwindow.

In embodiments of the present invention, motion noise can advantageouslybe correctly removed using low pass filters with suitable cut-offfrequencies depending on the activity.

In embodiments of the present invention, a Pan Tompkins derivativeoperation together with a peak elimination algorithm based on peakdistance and adaptive thresholds based on max-min value of a peakadvantageously helps the device and method to accurately identify peaksas step counts.

It will be appreciated by a person skilled in the art that numerousvariations and/or modifications may be made to the present invention asshown in the specific embodiments without departing from the spirit orscope of the invention as broadly described. The present embodimentsare, therefore, to be considered in all respects to be illustrative andnot restrictive. Also, the invention includes any combination offeatures, in particular any combination of features in the patentclaims, even if the feature or combination of features is not explicitlyspecified in the patent claims or the present embodiments.

For example, while a wrist-worn device is described in some embodiments,the device may be worn on the arms, hip, waist or foot of the user.Also, the device and method may perform only one of the activityclassification and the step counting as described above, or both at thesame time.

The invention claimed is:
 1. A device for classifying the activity of auser wearing the device, the device comprising: an accelerometer formeasuring accelerometer data for a plurality of axes; and a processorconfigured to: identify a most active one of the plurality of axes basedon the accelerometer data; and classify the activity of the user intodifferent non-stationary activities based on a signal amplitude of theaccelerometer data for the most active axis and respective differentthreshold values for the different non-stationary activities.
 2. Thedevice as claimed in claim 1, wherein the processor is configured toapply a smoothing to the accelerometer data prior to identifying themost active axis.
 3. The device as claimed in claim 2, wherein theprocessor is configured to identify the most active axis by determiningthe axis having the largest mean absolute amplitude of the smoothedsignal.
 4. The device as claimed in claim 1, wherein the processor isconfigured to classify the activity based on a maximum signal amplitudeof the accelerometer data for the most active axis.
 5. The device asclaimed in claim 1, wherein the processor is configured to identify themost active axis and to classify the activity of the user based onaccelerometer data measured over a predetermined processing window.
 6. Amethod for classifying the activity of a user, the method comprising:measuring accelerometer data for a plurality of axes; identifying a mostactive one of the plurality of axes based on the accelerometer data; andclassifying the activity of the user into different non-stationaryactivities based on a signal amplitude of the accelerometer data for themost active axis and respective different threshold values for thedifferent non-stationary activities.
 7. The method as claimed in claim6, comprising applying a smoothing to the accelerometer data prior toidentifying the most active axis.
 8. The method as claimed in claim 7,comprising identifying the most active axis by determining the axishaving the largest mean absolute value of the smoothed signal.
 9. Themethod as claimed in claim 6, comprising classifying the activity basedon a maximum absolute signal amplitude of the accelerometer data for themost active axis.
 10. The method as claimed in claim 6, comprisingidentifying the most active axis and classifying the activity of theuser based on accelerometer data measured over a predeterminedprocessing window.